The Path Kernel. - Andrea Baisero; Florian T. Pokorny; Danica Kragic; Carl Henrik Ek (ICPRAM, 2013)

The Path Kernel: A Novel Kernel for Sequential Data. - Andrea Baisero; Florian T. Pokorny; Danica Kragic; Carl Henrik Ek (ICPRAM (Selected Papers), 2013)

Temporal segmentation of pair-wise interaction phases in sequential manipulation demonstrations. - Andrea Baisero; Yoan Mollard; Manuel Lopes 001; Marc Toussaint; Ingo Lütkebohle (IROS, 2015)

Robot programming from demonstration, feedback and transfer. - Yoan Mollard; Thibaut Munzer; Andrea Baisero; Marc Toussaint; Manuel Lopes 001 (IROS, 2015)

On a Family of Decomposable Kernels on Sequences. - Andrea Baisero; Florian T. Pokorny; Carl Henrik Ek (CoRR, 2015)

Identification of Unmodeled Objects from Symbolic Descriptions. - Andrea Baisero; Stefan Otte; Peter Englert; Marc Toussaint (CoRR, 2017)

Active Goal Recognition. - Christopher Amato; Andrea Baisero (CoRR, 2019)

Learning Complementary Representations of the Past using Auxiliary Tasks in Partially Observable Reinforcement Learning. - Andrea Baisero; Christopher Amato (AAMAS, 2020)

Reconciling Rewards with Predictive State Representations. - Andrea Baisero; Christopher Amato (IJCAI, 2021)

Unbiased Asymmetric Actor-Critic for Partially Observable Reinforcement Learning. - Andrea Baisero; Christopher Amato (CoRR, 2021)

A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning. - Xueguang Lyu; Andrea Baisero; Yuchen Xiao; Christopher Amato (AAAI, 2022)

Unbiased Asymmetric Reinforcement Learning under Partial Observability. - Andrea Baisero; Christopher Amato (AAMAS, 2022)

Leveraging Fully Observable Policies for Learning under Partial Observability. - Hai Nguyen 001; Andrea Baisero; Dian Wang 001; Christopher Amato; Robert Platt 001 (CoRL, 2022)

Asymmetric DQN for partially observable reinforcement learning. - Andrea Baisero; Brett Daley; Christopher Amato (UAI, 2022)

Hierarchical Reinforcement Learning Under Mixed Observability. - Hai Nguyen 001; Zhihan Yang; Andrea Baisero; Xiao Ma; Robert Platt 001; Christopher Amato (WAFR, 2022)

Equivariant Reinforcement Learning under Partial Observability. - Hai Nguyen 001; Andrea Baisero; David Klee; Dian Wang 001; Robert Platt 001; Christopher Amato (CoRL, 2023)

On Centralized Critics in Multi-Agent Reinforcement Learning. - Xueguang Lyu; Andrea Baisero; Yuchen Xiao; Brett Daley; Christopher Amato (J. Artif. Intell. Res., 2023)

On Stateful Value Factorization in Multi-Agent Reinforcement Learning. - Enrico Marchesini; Andrea Baisero; Rupali Bathi; Christopher Amato (CoRR, 2024)

Role of State in Partially Observable Reinforcement Learning. - Andrea Baisero (AAMAS, 2025)

On Stateful Value Factorization in Multi-Agent Reinforcement Learning. - Enrico Marchesini; Andrea Baisero; Rupali Bhati; Christopher Amato (AAMAS, 2025)

Leveraging Fully-Observable Solutions for Improved Partially-Observable Offline Reinforcement Learning. - Chulabhaya Wijesundara; Andrea Baisero; Gregory D. Castañón; Alan Carlin; Robert Platt 001; Christopher Amato (AAMAS, 2025)

Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning. - Andrea Baisero; Rupali Bhati; Shuo Liu; Aathira Pillai; Christopher Amato (CoRR, 2025)